04356nam 22008175 450 991061928080332120240130150100.03-031-13584-910.1007/978-3-031-13584-2(MiAaPQ)EBC7119383(Au-PeEL)EBL7119383(CKB)25176461900041(DE-He213)978-3-031-13584-2(PPN)265856515(EXLCZ)992517646190004120221019d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierApplied Time Series Analysis and Forecasting with Python /by Changquan Huang, Alla Petukhina1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (377 pages)Statistics and Computing,2197-1706Print version: Huang, Changquan Applied Time Series Analysis and Forecasting with Python Cham : Springer International Publishing AG,c2022 9783031135835 Includes bibliographical references and index.1. Time Series Concepts and Python -- 2. Exploratory Time Series Data Analysis -- 3. Stationary Time Series Models -- 4. ARMA and ARIMA Modeling and Forecasting -- 5. Nonstationary Time Series Models -- 6. Financial Time Series and Related Models -- 7. Multivariate Time Series Analysis -- 8. State Space Models and Markov Switching Models -- 9. Nonstationarity and Cointegrations -- 10. Modern Machine Learning Methods for Time Series Analysis.This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.Statistics and Computing,2197-1706Time-series analysisStatisticsComputer programsEconometricsPython (Computer program language)Machine learningStatisticsTime Series AnalysisStatistical SoftwareEconometricsPythonMachine LearningStatistics in Business, Management, Economics, Finance, InsuranceAnàlisi de sèries temporalsthubPython (Llenguatge de programació)thubLlibres electrònicsthubTime-series analysis.StatisticsComputer programs.Econometrics.Python (Computer program language).Machine learning.Statistics.Time Series Analysis.Statistical Software.Econometrics.Python.Machine Learning.Statistics in Business, Management, Economics, Finance, Insurance.Anàlisi de sèries temporalsPython (Llenguatge de programació)813519.55Huang Changquan1262946Petukhina AllaMiAaPQMiAaPQMiAaPQBOOK9910619280803321Applied Time Series Analysis and Forecasting with Python2954958UNINA